Regression Analysis Homework and Course Request Information
Get ready to analyze regression data for homework, collect project data by the deadline, and attend to course request forms. Important updates and agenda items included. Don't miss the deadline!
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Homework (reg) Pg.204 #3.29(a,b), 3.31 (a,b) Collect project data by Wed. 2/6 Bring course request forms Wed. 2/6 Agenda Warm Up Checkup time Check, copies Slope & y-intercepts Housing data Update, copies Update w/ outliers removed Coefficient of variation Exit Pass Add Bring course request forms Wed. 2/5 10 min 5 min 20 min 15 min 10 min 10 min
x y Warm Up 1 10 Use the data to the right. 1. Calculate r. 2 8 3 5 4 6 2. On the same screen, find a and b . Use them to calculate the equation y=ax+b, for the line of best fit. 5 1 3. What is the predicted y if x=10? 4. If the predicted y is 2, what was x? 5. In the equation y = mx + b, if m=3, x=4, and y=17, what is b?
Course Request sheets Counselors available both lunches tomorrow, Wednesday, Thursday Important links: Summary of each department tinyurl.com/RCHS-catalog-summary Full course descriptions tinyurl.com/RCHS-catalog-2020 A-G catalog tinyurl.com/RCHS-A-G-2020 Make sure to: 1. Check your name, ID, email, and phone #. 2. Sign, and get your parent to sign. 3. Read prerequisites. 4. Choose backups carefully. Don t leave them blank. Bring to class Wednesday 2/5, so I can check it. Due to me this Friday 2/7. Consider suggesting this class (Statistics) to people. I strongly recommend you take Senior Seminar (or AVID 12).
Goodbye Einstein. Dead. Murdered.
P.1 y = 1.81x + 51.44
P.2 y = 1.75x + 52
P.3 y = 2.5x + 45.92
All students y = 2.81x + 43.118
Who dun it? TEACHER Saepan Cole VanBuskirk Frantz Ryan Hook Tan Ceo Hwang Colligan Burton Okita HEIGHT (in.) 67 64 74 70 65 69 61 68.5 61 75.5 64 66
Notes 3 of 3 Regression Line ? = ?? + ? Also called least-squares line , line of best fit Minimizes the sum of the squared deviations of predicted and observed y-values Used to predict y, given a value for x a slope or constant For every 1 [x], [y] goes up by _____. b y-intercept or amount When [x] is 0, [y] is ______. EXAMPLE. Handspan and height (Geogebra) Identify x and y!
Window/Door Briefly describe what the slope and y-intercept mean in context. 1. On your recent Unit 2 test, the relationship between your expected and actual scores was y = 0.5781x + 11.007, where expected scores is your explanatory variable. 2. From your survey data, the relationship between hours of sleep and GPA is y = -0.0028x + 3.6526, where sleep is your explanatory variable. 3. From your survey, the relationship between the hours you spent on a computer the last day of summer, and average hours studying per week, is y = 0.4897x + 5.6385, where hours on computer is the explanatory variable. 4. In 2012, the relationship between birth and death rates per 1000 people in all fifty US states was y = -0.3362x + 13.1295, where birth rates is the explanatory variable.
Complete in notes Housing Size and Price Does a house s size help us predict its price? 1. Determine the explanatory variable. 2. Check for outliers in both the explanatory and response variable. Remove them (along with their paired data) if you find them. 3. Make a scatterplot on your calculator. Calculate r. 4. Describe the scatterplot. Include r in your comments. You do not need to copy down the scatterplot. 5. Three years ago my wife and I bought a house in West Sacramento. It was recently re-appraised by the county inspector at $305,295. Guess how big our house is. 6. Describe three other explanatory variables that might influence a house s price.
Period 2 Size 7000 3264 3112 2941 2914 2891 2788 2549 2549 2549 2549 Price 3200000 610000 1198000 529000 529000 553000 620000 423900 423900 423900 423900 n=23 2549 2330 2330 2291 2076 2074 1700 1215 1162 1162 1162 936 423900 495000 495000 499800 588000 588800 400000 299900 359000 359000 359000 315000
Houses in West Sacramento (P.2) 3500000 3000000 2500000 2000000 y = 433.4x - 443211 R = 0.7882 Price 1500000 1000000 500000 0 0 1000 2000 3000 4000 5000 6000 7000 8000 -500000 Size
Period 2 Size 7000 3264 3112 2941 2914 2891 2788 2549 2549 2549 2549 Price 3200000 610000 1198000 529000 529000 553000 620000 423900 423900 423900 423900 n=23 2549 2330 2330 2291 2076 2074 1700 1215 1162 1162 1162 936 423900 495000 495000 499800 588000 588800 400000 299900 359000 359000 359000 315000
Houses in West Sacramento (P.2) 700000 600000 500000 400000 Price y = 104.89x + 233152 R = 0.5628 300000 200000 100000 0 0 500 1000 1500 2000 2500 3000 3500 Size
Houses in West Sacramento 700000 y = 104.05x + 160146 R = 0.5098 600000 500000 400000 Price 300000 200000 100000 0 0 1000 2000 3000 4000 5000 Size
Notes Coefficient of variation r2 Also coefficient of determination Proportion of the variation in y that can be explained by x. Example. Housing prices 2 of 3 You try: DOOR. When using sleep to predict GPA s, r2=0.0799 WINDOW. When using your expected scores on your Unit 2 test to predict your actual Unit 2 test scores, r2=0.3239
Project ideas Decent idea. Could get an A. Great idea. Interesting. Ambitious. Probably hasn t been done before. Really great idea. Definitely never done before. Not a good idea. Probably categorical.
Project #2: Predicting the Uncertain Today 1/31: Pick three different pairs of quantitative variables that you think might be correlated. See examples on the back. For each idea, briefly discuss . 1. Why you think the variables are correlated. 2. How you plan to collect your data. Will you find it online? Will you collect it yourself? How? Where? Wednesday 2/5: Collect a sample of 25+ pairs of data. Describe your process. If you collected your own data, describe your process, and how you tried to make the process fair and unbiased. Friday 2/7: Create numerical summaries for each of your two sets of data. Create 3 separate graphs 1) a graph for your explanatory variable, 2) a graph for your response variable, 3) a scatterplot for both variables. Monday 2/10: Write an analysis of your data. Comment on your scatterplot. Identify/ interpret r, and the least-squares equation. Interpret your residual plot. Comment broadly on your data. You may do a poster instead of a report.
Exit Pass Homework (reg) Pg.204 #3.29(a,b), 3.31 (a,b) Collect project data by Wed. 2/6 Bring course request forms Wed. 2/6 Can an SAT Mathematics score be used to predict an SAT Critical Reading score? 1. Are there any outliers? If yes, eliminate them. 2. Use your calculator to calculate the equation of the regression line. 3. I scored a 740 on the Math portion. Predict my Critical Reading score. (known as y) 4. Briefly explain what both your slope and y- intercept (from #2) mean in this context. Critical Reading 430 550 520 550 540 510 510 490 620 350 410 Math 370 550 520 450 570 520 540 520 540 610 540
